Exploring the Synergistic Effects of Dual‐Layer Electrodes for High Power Li‐Ion Batteries
Bibliographic record
Abstract
Abstract The electrification of the transport sector has created an increasing demand for lithium‐ion batteries that can provide high power intermittently while maintaining a high energy density. Given the difficulty in designing a single redox material with both high power and energy density, electrodes based on composites of several electroactive materials optimized for power or capacity are being studied extensively. Among others, fast‐charging LiFePO4 and high energy Li(NixMnyCoz)O2 are commonly employed in industrial cell manufacturing. This study focuses on comparing different approaches to combining these two active materials into a single electrode. These arrangements were compared using standard electrochemical (dis)charge procedures and using synchrotron X‐ray fluorescence to identify variations in solution concentration gradient formation. The electrochemical performance of the layered electrodes with the high‐power material on top is found to be enhanced relative to its blended electrode counterpart when (dis)charged at the same specific currents. These findings highlight dual‐layer lithium‐ion batteries as an inexpensive way of increasing energy and power density of lithium‐ion batteries as well as a model system to study and exploit the synergistic effects of blended electrodes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".